umx_score_scale: Score a psychometric scale by summing normal and reversed items.

Description

Use this function to generate scores as the appropriate sum of responses to the normal and reversed items in a scale.

Items must be named on the pattern basename + N + suffix, where base is the prefix common to all item (column) names, N is item number in the scale, and suffix an optional trail (like "_T1").

pos and rev are vectors of the item numbers for the normal and reverse-scored item numbers.

To reverse items, the function uses max and min as the lowest and highest possible response scores to compute how to reverse items.

note: min defaults to 1. TIP: If you have strings, umx_score_scale will work (use mapStrings = ). BUT if you want to make a numeric copy, use umx_strings2numeric

Usage

umx_score_scale(
  base = NULL,
  pos = NULL,
  rev = NULL,
  min = 1,
  max = NULL,
  data = NULL,
  score = c("total", "proportionCorrect", "errors", "mean", "max", "factor"),
  name = NULL,
  na.rm = TRUE,
  minManifests = NA,
  alpha = FALSE,
  mapStrings = NULL,
  correctAnswer = NULL,
  omegaNfactors = 1,
  digits = 2,
  verbose = FALSE,
  suffix = ""
)

Value

scores

Arguments

base: String common to all item names.
pos: The positive-scored item numbers.
rev: The reverse-scored item numbers.
min: Minimum legal response value (default = 1). Not implemented for values other than 1 so far...
max: Maximum legal response value (also used to compute reversed item values).
data: The data frame
score: Score total (default), proportionCorrect, errors, mean, max, or factor scores
name: The name of the scale to be returned. Defaults to "base_score"
na.rm: Whether to delete NAs when computing scores (Default = TRUE) Note: Choice affects mean!
minManifests: How many missing items to tolerate for an individual (when score = factor)
alpha: print Reliability (omega and Cronbach's alpha) (TRUE)
mapStrings: Recoding input like "No"/"Maybe"/"Yes" into numeric values (0,1,2)
correctAnswer: Use when scoring items with one correct response (1/0).
omegaNfactors: Number of factors for the omega reliability (default = 1)
digits: Rounding for omega etc. (default 2)
verbose: Whether to print the whole omega output (FALSE)
suffix: (if dealing with, e.g. "_T1")

Details

In the presence of NAs, score= "mean" and score = "totals" both return NA unless na.rm = TRUE. score = "max", ignores NAs no matter what.

References

Revelle, W. (2022) psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 2.2.9.

McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23, 412-433. tools:::Rd_expr_doi("10.1037/met0000144").

Examples

Run this code

library(psych)
library(psychTools)
data(bfi)

# ==============================
# = Score Agreeableness totals =
# ==============================

# Handscore subject 1
# A1(R)+A2+A3+A4+A5 = (6+1)-2 +4+3+4+4 = 20

tmp = umx_score_scale(base = "A", pos = 2:5, rev = 1, max = 6, data= bfi, name = "A")
tmp[1, namez(tmp, "A",ignore.case = FALSE)]
#  A1 A2 A3 A4 A5  A
#  2  4  3  4  4  20

# ====================
# = Request the mean =
# ====================
tmp = umx_score_scale(name = "A", base = "A", 
   pos = 2:5, rev = 1, max = 6, data= bfi, score="mean")
tmp$A[1] # = 4

# ========================
# = Request factor score =
# ========================
if (FALSE) {
tmp = umx_score_scale(name = "A", base = "A", pos = 2:5, rev = 1,
   max = 6, score = "factor", minManifests = 4, data= bfi)
#            g
# A2 0.6574826
# A3 0.7581274
# A4 0.4814788
# A5 0.6272332
# A1 0.3736021

# ==================
# = Request alpha  =
# ==================

tmp=umx_score_scale(base="A", pos=2:5, rev=1, max=6, data=bfi, alpha=TRUE)
# omega t = 0.72
}

# ==================
# = na.rm = TRUE ! =
# ==================
tmpDF = bfi
tmpDF[1, "A1"] = NA
tmp = umx_score_scale("A", pos = 2:5, rev = 1, max = 6, data= tmpDF, score="mean")
tmp$A_score[1] # 3.75

tmp= umx_score_scale("A", pos= 2:5, rev= 1, max = 6, data = tmpDF,
   score="mean", na.rm=FALSE)
tmp$A_score[1] # NA (reject cases with missing items)

# ===============
# = Score = max =
# ===============
tmp = umx_score_scale("A", pos = 2:5, rev = 1, max = 6,
  data = bfi, name = "A", score = "max")
tmp$A[1] # Subject 1 max = 5 (reversed) item 1

# Default scale name
tmp = umx_score_scale("E", pos = 3:5, rev = 1:2, max = 6, 
   data= tmp, score = "mean", na.rm = FALSE)
tmp$E_score[1]

# Using @BillRevelle's psych package: More diagnostics, including alpha
scores= psych::scoreItems(items = bfi, min = 1, max = 6, keys = list(
	E = c("-E1","-E2", "E3", "E4", "E5"),
	A = c("-A1", "A2", "A3", "A4", "A5")
))
summary(scores)
scores$scores[1, ]
#  E   A 
# 3.8 4.0 

# Compare output
# (note, by default psych::scoreItems replaces NAs with the sample median...)
RevelleE = as.numeric(scores$scores[,"E"])
RevelleE == tmp[,"E_score"]

# =======================
# = MapStrings examples =
# =======================
mapStrings = c(
   "Very Inaccurate", "Moderately Inaccurate", 
   "Slightly Inaccurate", "Slightly Accurate",
   "Moderately Accurate", "Very Accurate")
bfi$As1 = factor(bfi$A1, levels = 1:6, labels = mapStrings)
bfi$As2 = factor(bfi$A2, levels = 1:6, labels = mapStrings)
bfi$As3 = factor(bfi$A3, levels = 1:6, labels = mapStrings)
bfi$As4 = factor(bfi$A4, levels = 1:6, labels = mapStrings)
bfi$As5 = factor(bfi$A5, levels = 1:6, labels = mapStrings)
bfi= umx_score_scale(name="A" , base="A", pos=2:5, rev=1, max=6, data=bfi)
bfi= umx_score_scale(name="As", base="As", pos=2:5, rev=1, mapStrings = mapStrings, data= bfi)

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